Simon Stephan

Research Scientist in the field of Cognitive Science at the University of Göttingen

About me

I am a post-doctoral researcher at the University of Göttingen working in Michael Waldmann’s Lab of Cognitive and Decision Sciences. I’m very much interested in how minds learn and reason about the causal relationships in the world. Given that causal relations are neither directly observable nor logically deducible, how do reasoners manage to learn about causes and effect so successfully? How do they use this knowledge to make predictions, diagnoses, or to explain things? I’m also fascinated by the more general question of how minds form and use categories.

On this website I share information about my academic life. You’ll find an up-to-date CV, my publication list, and information about recent/ upcoming presentations (e.g., conference talks).

Interests

  • Computational Cognitive Science
  • Philosophy of Science
  • Causal Cognition
  • Learning and Reasoning
  • Statistical Inference
  • Open Science

Education

  • PhD in Psychology, 2019 University of Göttingen
  • MSc in Psychology, 2014 University of Göttingen
  • BSc in Psychology, 2012 University of Göttingen

Grants, Honors & Awards

  • 2017 Computational Modeling Prize - Higher Level Cognition Awarded by the Cognitive Science Society
  • 2016 Leibniz-ScienceCampus Grant, Project: The relationship between causal and moral judgments
  • 2015 Leibniz-ScienceCampus Grant, Project: The role of intentions in children’s and adult’s causal ascriptions

Teaching

  • Winter terms (2014/15 until 2021/22): Quantitative Methods I Seminar As part of the first year undergraduate psychology statistics class

  • Summer terms (2015 until 2022): Quantitative Methods II Seminar As part of the first year undergraduate psychology statistics class

  • Winter term 2022/23: Seminar on the principles of learning and behavior As part of the second year undergraduate psychology module “Allgemeine Psychology II” (General Psychology II)

The seminar Quantitative Methods I covers basics of research design and the application of hypothesis testing, data visualisation, probability theory, descriptive and inferential data analysis, and power analyses. Quantitative Methods II focuses on the General Linear Model and its applications (regression, ANOVA, contrast analyses, multilevel models). Students learn to apply these methods with R and RStudio.

An overview of the teaching resources (including teaching videos) is given at: https://quantigoettingen.github.io/quantigoettingen

I also supervised a number of Bachelor and Master projects (see my CV for a list).


Selected Publications


Tutorials

I’m passionate about online experiments and created a small series of YouTube-Tutorials where I show how to create a “typical cognitive science” experiment using JsPsych.




Recent Talks & Conference Presentations

2022

  • Flash talk presentation at CogSci 2022: The Perceived Dilution of Causal Strength (July 2022, Toronto [remote])

  • Poster presentation at SPP & ESPP: The Perceived Dilution of Causal Strength (July 2022, Milan, Italy)

  • Invited Talk [virtual] at the London Judgment and Decision Making Seminar: The interplay between covariation, temporal, and mechanism information in singular causation judgments (Jan 2021, London)

  • Invited Talk [virtual] at the Computational Cognitive Science Group at the University of Edinburgh: The interplay between covariation, temporal, and mechanism information in singular causation judgments (Jan 2021, London)

2021

  • Poster presentation [virtual] at CogSci 2021: Evaluating general versus singular causal prevention (July 2021, Vienna)

  • Invited Talk [virtual] at Becog Colloquium: Computational/ mathematical modeling in cognitive science (April 2021, Göttingen)

  • Invited Talk [virtual] at the CPI Lab Tübingen: The interplay between covariation, temporal, and mechanism information in singular causation judgments (Jan 2021, London)

2020

  • Talk [virtual] at CIC-Lab: Interpolating Causal Mechanisms - The Paradox of Knowing More (Apr 2020, Stanford University)

2019

  • Talk at CRISP-Lab: What made this happen? A computational modeling approach to answering causal queries about singular cases (Dez 2019, Heidelberg)

  • Talk at ANaP-Lab: Computational Modeling and Progress in Cognitive Science (Oct 2019, Göttingen)

  • Talk at ESPP 2019: The Role of Effect and Sample Size in Causal Induction (Sep 2019, Athens)

  • Poster at Euro CogSci 2019: The Role of Effect and Sample Size in Causal Induction (Sep 2019, Bochum)


All publications

2024

  • Stephan, S. (2024). Reasoning about actual causation in reversible and irreversible causal structures. Journal of Experimental Psychology: Learning, Memory, and Cognition. Advance online publication

2023

  • Stephan, S. (2023). Revisiting the narrow latent scope bias in explanatory reasoning. Cognition, 241, 105630.

  • Stephan, S., Engelmann, N., & Waldmann, M. R. (2023). The perceived dilution of causal strength. Cognitive Psychology, 140, 101540.

2022

  • Stephan, S., & Waldmann, M. R. (2022). The interplay between covariation, temporal, and mechanism information in singular causation judgments. In A. Wiegmann, & P. Willemsen (Eds.). Advances in Experimental Philosophy of Causation. London, UK: Bloomsbury Press.

  • Stephan, S., & Waldmann, M. R. (2022). The role of mechanism knowledge in singular causation judgments. Cognition, 218, 104924.

2021

  • Skovgaard-Olsen, N., Stephan, S., & Waldmann, M. R. (2021). Conditionals and the hierarchy of causal queries. Journal of Experimental Psychology: General, 150, 2472–2505.

  • Gerstenberg, T., & Stephan, S. (2021). A counterfactual simulation model of causation by omission. Cognition, 216, 104842.

  • Stephan, S., Placì, Sarah & Waldmann, M. R. (2021). Evaluating general versus singular causal prevention. In T. Fitch, C. Lamm, H. Leder, & K. Tessmar (Eds.), Proceedings of the 43rd Annual Conference of the Cognitive Science Society. (pp. 1402–1408). Austin, TX: Cognitive Science Society.

  • Stephan, S., Tentori, K., Pighin, S., & Waldmann, M. R. (2021). Interpolating causal mechanisms: The paradox of knowing more. Journal of Experimental Psychology: General, 150(8), 1500-1527.

2020

  • Stephan, S., & Waldmann, M. R. (2020). Causal scope and causal strength: The number of potential effects of a cause influences causal strength estimates. In S. Denison., M. Mack, Y. Xu, & B.C. Armstrong (Eds.), Proceedings of the 42th Annual Conference of the Cognitive Science Society (pp. 3426 - 3432). Austin, TX: Cognitive Science Society.

  • Stephan, S., & Waldmann, M. R. (2020). On causal claims, contingencies, and inference: How causal terminology affects what we think about the strength of causal links. In S. Denison., M. Mack, Y. Xu, & B.C. Armstrong (Eds.), Proceedings of the 42th Annual Conference of the Cognitive Science Society (pp. 3419 - 3425). Austin, TX: Cognitive Science Society.

  • Stephan, S., Mayrhofer, R., & Waldmann, M. R. (2020). Time and singular causation - a computational model. Cognitive Science, 44, e12871.

2019

  • Stephan, S. (2019). Answering causal queries about singular cases - an evaluation of a new computational model. (Dissertation)

2018

  • Stephan, S., Mayrhofer, R., & Waldmann, M. R. (2018). Assessing singular causation: The role of causal latencies. In T.T. Rogers, M. Rau, X. Zhu, & C. W. Kalish (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (pp. 1080 - 1085). Austin, TX: Cognitive Science Society.

  • Stephan, S., & Waldmann, M. R. (2018). Preemption in singular causation judgments: A computational model. Topics in Cognitive Science, 10, 242–257.

2017

  • Stephan, S., & Waldmann, M. R. (2017). Preemption in singular causation judgments: A computational model. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (pp. 1126-1131). Austin, TX: Cognitive Science Society. (Computational Modeling: Higher Level Cognition Award of the Cognitive Science Society).

  • Stephan, S., Willemsen, P. & Gerstenberg, T. (2017). Marbles in inaction: Counterfactual simulation and causation by omission. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (pp. 1132-1137). Austin, TX: Cognitive Science Society.

2016

  • Nagel, J., & Stephan, S. (2016). Explanations in causal chains: Selecting distal causes requires exportable mechanisms. In A. Papafragou, D. Grodner, D. Mirman, & J.C. Trueswell (Eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society (pp. 806-811). Austin, TX: Cognitive Science Society.

  • Stephan, S., & Waldmann, M. R. (2016). Answering causal queries about singular cases. In A. Papafragou, D. Grodner, D. Mirman, & J.C. Trueswell (Eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society (pp. 2795-2801). Austin, TX: Cognitive Science Society.

2015

  • Nagel, J., & Stephan, S.. (2015). Mediators or alternative explanations: Transitivity in human-mediated causal chains. In D. C. Noelle, R. Dale, A. S. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings, & P. P. Maglio (Eds.), Proceedings of the 37th Annual Meeting of the Cognitive Science Society (pp. 1691-1696). Austin, TX: Cognitive Science Society.

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